Skip to main content
SpringerLink
Log in

Predicting the 3D Structure of RNA from Sequence

  • Living reference work entry
  • First Online:
Handbook of Chemical Biology of Nucleic Acids

  • 114 Accesses

Abstract

Many RNA molecules, particularly non-coding RNA molecules, fold back on themselves to make basepairs, base stacks, and other contacts, and the 3D structures that they form are essential for the performance of their functions. Experimentally determining the 3D structure of an RNA molecule is difficult and time consuming, so it is desirable to be able to predict the 3D structure that an RNA molecule will fold into based only on the molecule’s sequence. We review RNA 3D structure prediction techniques that have been benchmarked in the most recent RNA-Puzzles competition and survey new tools that have been developed since then. The evolution of tools that predict RNA 3D structures from sequence has been similar to that of tools for the prediction of protein 3D structures, and it seems we might be on the precipice of a leap forward in RNA 3D structure prediction from tools using machine learning and deep neural networks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Similar content being viewed by others

References

  • Antczak M, Popenda M, Zok T, Sarzynska J, Ratajczak T, Tomczyk K, Adamiak RW, Szachniuk M (2016) New functionality of RNAComposer: application to shape the axis of miR160 precursor structure. Acta Biochim Pol 63(4):737–744. https://doi.org/10.18388/abp.2016_1329

    Article  CAS  Google Scholar 

  • Baulin E, Metelev V, Bogdanov A (2020) Base-intercalated and base-wedged stacking elements in 3D-structure of RNA and RNA–protein complexes. Nucleic Acids Res 48(15):8675–8685

    Article  CAS  Google Scholar 

  • Boniecki MJ, Lach G, Dawson WK, Tomala K, Lukasz P, Soltysinski T, Rother KM, Bujnicki JM (2016) SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction. Nucleic Acids Res 44(7):e63. https://doi.org/10.1093/nar/gkv1479

    Article  CAS  Google Scholar 

  • Chen J, Hu Z, Sun S, Tan Q, Wang Y, Yu Q, Zong L, Hong L, Xiao J, King I (2022) Interpretable RNA foundation model from unannotated data for highly accurate RNA structure and function predictions. arXiv Preprint arXiv:2204.00300

    Google Scholar 

  • Corley M, Burns MC, Yeo GW (2020) How RNA-binding proteins interact with RNA: molecules and mechanisms. Mol Cell 78(1):9–29

    Article  CAS  Google Scholar 

  • Cruz JA, Westhof E (2011) Sequence-based identification of 3D structural modules in RNA with RMDetect. Nat Methods 8(6):513–519

    Article  CAS  Google Scholar 

  • Cruz JA, Blanchet M, Boniecki M, Bujnicki JM, Chen S, Cao S, Das R, Ding F, Dokholyan NV, Flores SC (2012) RNA-Puzzles: a CASP-like evaluation of RNA three-dimensional structure prediction. RNA 18(4):610–625

    Article  CAS  Google Scholar 

  • Das R (2021) RNA structure: a renaissance begins? Nat Methods 18(5):439

    Article  CAS  Google Scholar 

  • Das R, Karanicolas J, Baker D (2010) Atomic accuracy in predicting and designing noncanonical RNA structure. Nat Methods 7(4):291–294

    Article  CAS  Google Scholar 

  • Dethoff EA, Petzold K, Chugh J, Casiano-Negroni A, Al-Hashimi HM (2012) Visualizing transient low-populated structures of RNA. Nature 491(7426):724–728

    Article  CAS  Google Scholar 

  • Ding J, Lee YT, Bhandari Y, Fan L, Schwieters C, Yu P, Tarasov S, Stagno J, Ma B, Nussinov R, Rein A (2023) Visualizing RNA conformational and architectural heterogeneity in solution (under publication)

    Google Scholar 

  • Djebali S, Davis CA, Merkel A, Dobin A, Lassmann T, Mortazavi A, Tanzer A, Lagarde J, Lin W, Schlesinger F (2012) Landscape of transcription in human cells. Nature 489(7414):101–108

    Article  CAS  Google Scholar 

  • Evans R, O’Neill M, Pritzel A, Antropova N, Senior AW, Green T, Žídek A, Bates R, Blackwell S, Yim J (2021) Protein complex prediction with AlphaFold-Multimer. BioRxiv

    Google Scholar 

  • Ganser LR, Kelly ML, Herschlag D, Al-Hashimi HM (2019) The roles of structural dynamics in the cellular functions of RNAs. Nat Rev Mol Cell Biol 20(8):474–489

    Article  CAS  Google Scholar 

  • Gebert LF, MacRae IJ (2019) Regulation of microRNA function in animals. Nat Rev Mol Cell Biol 20(1):21–37

    Article  CAS  Google Scholar 

  • Houseley J, Tollervey D (2009) The many pathways of RNA degradation. Cell 136(4):763–776

    Article  CAS  Google Scholar 

  • Hurst T, Chen S (2021) Deciphering nucleotide modification-induced structure and stability changes. RNA Biol 18(11):1920–1930

    Article  CAS  Google Scholar 

  • Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596(7873):583–589

    Article  CAS  Google Scholar 

  • Kalvari I, Nawrocki EP, Ontiveros-Palacios N, Argasinska J, Lamkiewicz K, Marz M, Griffiths-Jones S, Toffano-Nioche C, Gautheret D, Weinberg Z (2021) Rfam 14: expanded coverage of metagenomic, viral and microRNA families. Nucleic Acids Res 49(D1):D192–D200

    Article  CAS  Google Scholar 

  • Knott GJ, Doudna JA (2018) CRISPR-Cas guides the future of genetic engineering. Science 361(6405):866–869

    Article  CAS  Google Scholar 

  • Könst ZA, Szklarski AR, Pellegrino S, Michalak SE, Meyer M, Zanette C, Cencic R, Nam S, Voora VK, Horne DA (2017) Synthesis facilitates an understanding of the structural basis for translation inhibition by the lissoclimides. Nat Chem 9(11):1140–1149

    Article  Google Scholar 

  • Krokhotin A, Houlihan K, Dokholyan NV (2015) iFoldRNA v2: folding RNA with constraints. Bioinformatics 31(17):2891–2893

    Article  CAS  Google Scholar 

  • Kryshtafovych A, Schwede T, Topf M, Fidelis K, Moult J (2019) Critical assessment of methods of protein structure prediction (CASP)—Round XIII. Proteins 87(12):1011–1020

    Article  CAS  Google Scholar 

  • Kryshtafovych A, Schwede T, Topf M, Fidelis K, Moult J (2021) Critical assessment of methods of protein structure prediction (CASP)—Round XIV. Proteins 89(12):1607–1617

    Article  CAS  Google Scholar 

  • Lensink MF, Brysbaert G, Mauri T, Nadzirin N, Velankar S, Chaleil RA, Clarence T, Bates PA, Kong R, Liu B (2021) Prediction of protein assemblies, the next frontier: the CASP14-CAPRI experiment. Proteins 89(12):1800–1823

    Article  CAS  Google Scholar 

  • Leontis NB, Stombaugh J, Westhof E (2002) The non-Watson–Crick base pairs and their associated isostericity matrices. Nucleic Acids Res 30(16):3497–3531

    Article  CAS  Google Scholar 

  • Li J, Chen S (2021) RNA 3D structure prediction using coarse-grained models. Front Mol Biosci 8:720937

    Article  CAS  Google Scholar 

  • Li B, Cao Y, Westhof E, Miao Z (2020) Advances in RNA 3D structure modeling using experimental data. Front Genet 11:574485

    Article  CAS  Google Scholar 

  • Mattick JS, Makunin IV (2006) Non-coding RNA. Hum Mol Genet 15(suppl_1):R17–R29

    Article  CAS  Google Scholar 

  • Miao Z, Westhof E (2017) RNA structure: advances and assessment of 3D structure prediction. Annu Rev Biophys 46:483–503

    Article  CAS  Google Scholar 

  • Miao Z, Adamiak RW, Antczak M, Boniecki MJ, Bujnicki J, Chen S, Cheng CY, Cheng Y, Chou F, Das R (2020) RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers. RNA 26(8):982–995

    Article  CAS  Google Scholar 

  • Mlýnský V, Janeček M, Kührová P, Fröhlking T, Otyepka M, Bussi G, Banáš P, Šponer J (2022) Toward convergence in folding simulations of RNA tetraloops: comparison of enhanced sampling techniques and effects of force field modifications. J Chem Theory Comput 18(4):2642–2656

    Article  Google Scholar 

  • Nawrocki EP, Eddy SR (2013) Infernal 1.1: 100-fold faster RNA homology searches. Bioinformatics 29(22):2933–2935

    Article  CAS  Google Scholar 

  • Parisien M, Major F (2008) The MC-fold and MC-Sym pipeline infers RNA structure from sequence data. Nature 452(7183):51–55

    Article  CAS  Google Scholar 

  • Pearce R, Zhang Y (2021) Toward the solution of the protein structure prediction problem. J Biol Chem 297(1):100870

    Article  CAS  Google Scholar 

  • Pearce R, Omenn GS, Zhang Y (2022) De Novo RNA tertiary structure prediction at atomic resolution using geometric potentials from deep learning. BioRxiv. https://www.biorxiv.org/content/10.1101/2022.05.15.491755v1

  • Petrov AI, Zirbel CL, Leontis NB (2013) Automated classification of RNA 3D motifs and the RNA 3D Motif Atlas. RNA 19(10):1327–1340

    Article  CAS  Google Scholar 

  • Ponce-Salvatierra A, Astha A, Merdas K, Nithin C, Ghosh P, Mukherjee S, Bujnicki JM (2019) Computational modeling of RNA 3D structure based on experimental data. Biosci Rep 39(2):BSR20180430

    Article  Google Scholar 

  • Popenda M, Szachniuk M, Blazewicz M, Wasik S, Burke EK, Blazewicz J, Adamiak RW (2010) RNA FRABASE 2.0: an advanced web-accessible database with the capacity to search the three-dimensional fragments within RNA structures. Springer Science and Business Media LLC. https://doi.org/10.1186/1471-2105-11-231

    Book  Google Scholar 

  • Pucci F, Zerihun MB, Peter EK, Schug A (2020) Evaluating DCA-based method performances for RNA contact prediction by a well-curated data set. RNA 26(7):794–802

    Article  CAS  Google Scholar 

  • Reinharz V, Soulé A, Westhof E, Waldispühl J, Denise A (2018) Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families. Nucleic Acids Res 46(8):3841–3851

    Article  CAS  Google Scholar 

  • Remmert M, Biegert A, Hauser A, Söding J (2012) HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat Methods 9(2):173–175

    Google Scholar 

  • Rivas E (2021) Evolutionary conservation of RNA sequence and structure. Wiley Interdiscip Rev RNA 12(5):e1649

    Article  CAS  Google Scholar 

  • RNAcentral Consortium (2021) RNAcentral 2021: secondary structure integration, improved sequence search and new member databases. Nucleic Acids Res 49(D1):D212–D220

    Article  Google Scholar 

  • Roll J, Zirbel CL, Sweeney B, Petrov AI, Leontis N (2016) JAR3D webserver: scoring and aligning RNA loop sequences to known 3D motifs. Nucleic Acids Res 44(W1):W320–W327

    Article  CAS  Google Scholar 

  • Schärfen L, Neugebauer KM (2021) Transcription regulation through nascent RNA folding. J Mol Biol 433(14):166975

    Article  Google Scholar 

  • Seemann SE, Menzel P, Backofen R, Gorodkin J (2011) The PETfold and PETcofold web servers for intra-and intermolecular structures of multiple RNA sequences. Nucleic Acids Res 39(suppl_2):W107–W111

    Article  CAS  Google Scholar 

  • Senior AW, Evans R, Jumper J, Kirkpatrick J, Sifre L, Green T, Qin C, Žídek A, Nelson AW, Bridgland A (2020) Improved protein structure prediction using potentials from deep learning. Nature 577(7792):706–710

    Article  CAS  Google Scholar 

  • Shen T, Hu Z, Peng Z, Chen J, Xiong P, Hong L, Zheng L, Wang Y, King I, Wang S (2022) E2Efold-3D: end-to-end deep learning method for accurate de novo RNA 3D structure prediction. arXiv Preprint arXiv:2207.01586

    Google Scholar 

  • Singh J, Paliwal K, Litfin T, Singh J, Zhou Y (2022) Predicting RNA distance-based contact maps by integrated deep learning on physics-inferred secondary structure and evolutionary-derived mutational coupling. Bioinformatics 38:3900–3910

    Article  CAS  Google Scholar 

  • Sripakdeevong P, Cevec M, Chang AT, Erat MC, Ziegeler M, Zhao Q, Fox GE, Gao X, Kennedy SD, Kierzek R (2014) Structure determination of noncanonical RNA motifs guided by 1H NMR chemical shifts. Nat Methods 11(4):413–416

    Article  CAS  Google Scholar 

  • Stombaugh J, Zirbel CL, Westhof E, Leontis NB (2009) Frequency and isostericity of RNA base pairs. Nucleic Acids Res 37(7):2294–2312

    Article  CAS  Google Scholar 

  • Sun S, Wang W, Peng Z, Yang J (2021) RNA inter-nucleotide 3D closeness prediction by deep residual neural networks. Bioinformatics 37(8):1093–1098

    Article  CAS  Google Scholar 

  • Townshend RJ, Eismann S, Watkins AM, Rangan R, Karelina M, Das R, Dror RO (2021) Geometric deep learning of RNA structure. Science 373(6558):1047–1051

    Article  CAS  Google Scholar 

  • Tunyasuvunakool K, Adler J, Wu Z, Green T, Zielinski M, Žídek A, Bridgland A, Cowie A, Meyer C, Laydon A (2021) Highly accurate protein structure prediction for the human proteome. Nature 596(7873):590–596

    Article  CAS  Google Scholar 

  • Vicens Q, Kieft JS (2022) Thoughts on how to think (and talk) about RNA structure. Proc Natl Acad Sci 119(17):e2112677119

    Article  CAS  Google Scholar 

  • Wang J, Wang J, Huang Y, Xiao Y (2019) 3dRNA v2.0: an updated web server for RNA 3D structure prediction. Int J Mol Sci 20(17):4116

    Article  CAS  Google Scholar 

  • Watkins AM, Rangan R, Das R (2020) FARFAR2: improved de novo Rosetta prediction of complex global RNA folds. Structure (London) 28(8):963–976.e6. https://doi.org/10.1016/j.str.2020.05.011

    Article  CAS  Google Scholar 

  • Weeks KM (2021) Piercing the fog of the RNA structure-ome. Science 373(6558):964–965

    Article  CAS  Google Scholar 

  • Wiedemann J, Kaczor J, Milostan M, Zok T, Blazewicz J, Szachniuk M, Antczak M (2022) RNAloops: a database of RNA multiloops. Bioinformatics 38(17):4200–4205

    Article  CAS  Google Scholar 

  • wwPDB Consortium (2019) Protein Data Bank: the single global archive for 3D macromolecular structure data. Nucleic Acids Res 47(D1):D520–D528

    Article  Google Scholar 

  • Yang J, Anishchenko I, Park H, Peng Z, Ovchinnikov S, Baker D (2020) Improved protein structure prediction using predicted interresidue orientations. Proc Natl Acad Sci 117(3):1496–1503

    Article  CAS  Google Scholar 

  • Yang M, Zhu P, Cheema J, Bloomer R, Mikulski P, Liu Q, Zhang Y, Dean C, Ding Y (2022) In vivo single-molecule analysis reveals COOLAIR RNA structural diversity. Nature 609(7926):394–399

    Article  CAS  Google Scholar 

  • Zhang P, Wu W, Chen Q, Chen M (2019) Non-coding RNAs and their integrated networks. J Integr Bioinform 16(3):20190027

    Article  Google Scholar 

  • Zhang T, Singh J, Litfin T, Zhan J, Paliwal K, Zhou Y (2021) RNAcmap: a fully automatic pipeline for predicting contact maps of RNAs by evolutionary coupling analysis. Bioinformatics 37(20):3494–3500

    Article  CAS  Google Scholar 

  • Zhao C, Xu X, Chen S (2017) Predicting RNA structure with Vfold. In: Functional genomics. Springer, pp 3–15

    Chapter  Google Scholar 

  • Zirbel CL, Šponer JE, Šponer J, Stombaugh J, Leontis NB (2009) Classification and energetics of the base-phosphate interactions in RNA. Nucleic Acids Res 37(15):4898–4918

    Article  CAS  Google Scholar 

  • Zirbel CL, Roll J, Sweeney BA, Petrov AI, Pirrung M, Leontis NB (2015) Identifying novel sequence variants of RNA 3D motifs. Nucleic Acids Res 43(15):7504–7520

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Findlay, Findlay, OH, USA

    James Roll

  2. Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, OH, USA

    Craig L. Zirbel

Authors
  1. James Roll
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Craig L. Zirbel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Craig L. Zirbel .

Editor information

Editors and Affiliations

  1. Frontier Institute for Biomolecular Engineering Research (FIBER), Konan University, Kobe, Japan

    Naoki Sugimoto

Section Editor information

  1. Slovenian NMR Centre, National Institute of Chemistry, Ljubljana, Slovenia

    Janez Plavec

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Singapore Pte Ltd.

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Roll, J., Zirbel, C.L. (2023). Predicting the 3D Structure of RNA from Sequence. In: Sugimoto, N. (eds) Handbook of Chemical Biology of Nucleic Acids. Springer, Singapore. https://doi.org/10.1007/978-981-16-1313-5_14-1

Download citation

  • DOI: https://doi.org/10.1007/978-981-16-1313-5_14-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1313-5

  • Online ISBN: 978-981-16-1313-5

  • eBook Packages: Springer Reference Chemistry and Mat. ScienceReference Module Physical and Materials ScienceReference Module Chemistry, Materials and Physics

Publish with us

Policies and ethics

Search

Navigation